The Brownlow medal is the highest individual honour that can be bestowed upon an AFL footballer. In each of the 176 home and away matches for a season, votes are assigned to the three best players (3 - first, 2 - second, 1- third) by the umpires that preside over the game. With the use of an ordinal logistic regression model retrospectively applied to past data, Bailey and Clarke (2002) constructed a l3 variable model that has successfully been used to identify the leading candidates for each Brownlow medal count. This paper seeks to build on this work by identifying an additional 12 variables relating to player and match statistics that are highly significant predictors of the number of votes received (p<0.001). We then use a range of various measures of goodness of fit, to explore the difference between statistical significance and practical significance by determining how much benefit each additional variable adds to the prediction process. By varying the size of the training data and holdout samples it is possible to determine the optimal size for training data, along with measuring the detrimental effects of over-fitting the data. Whilst it is possible to use mathematical models to aid in the prediction of the Brownlow medal, there is clearly a limit to the benefit achieved. This paper identifies this limit and determines how much data are required to achieve the optimal solution.